PSyclone VS NumPy

I remember they had projects like LFRic, GungHo, and PSyclone (https://github.com/stfc/PSyclone). This last one, I think was intended to allow its users to write code in Python, in a notebook, and then translate it into Fortran. I don't know if this is something that could be used for trying to combine Python with some FP library and then compile to Fortran, or maybe use a similar approach and transpile parts of Ocaml/Haskell/lisp/etc into Fortran...

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]